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By Jan Schröder

Fraud and manipulation in prediction markets are systematic result of incentive incompatibility, which, if current, must be detected and balanced. "Manipulations in Prediction Markets" offers a serious perception into manipulations which are probably to happen in prediction markets. In a basic method the e-book discusses the difficulty of incentives in markets and the breakdown of the motivation method. in this foundation a brand new manner of detecting abnormal buying and selling behaviour is brought.

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For the prediction market and its outcome in precise predictions the best traders are the ones that truthfully revealed their true opinion at any point in time during the market. So after market close the system should honour this truthful information revealing strategy by getting best rewards to players that did so. 1. Real money market in a closed world scenario This scenario describes a prediction market where trader buy and sell shares within a market by using real world money. However, the prediction market is either isolated from the outside world or does not influence the events in the outside world (closed world assumption).

2. As participants in a market are assumed to be profit maximising, these changes in the incentive system are usually exploited by the participants expecting a higher profit by doing so. These actions are usually forbidden by market rules. They are thus irregular and called fraudulent or are referred to as manipulations. Usually incentive compatibility is maintained by a system design that makes it more lucrative to play by the rules. This can be achieved by the traders having systematic higher rewards when playing conforming to the rules then when playing against them.

Thus a component with a low absolute value will be represented as a light saturated colour or even gray, while a component with a high absolute value will show a more or less saturated colour according to its π phase. 2eiπ will show a low saturated turquoise. 3. 2 where the eigenvectors xi are the rows of a graphical array (x1 , . . , xn )T . The eigenvectors are sorted by the absolute value of each eigenvector’s corresponding eigenvalue in descending order. For the sake of clarity of the phase information the saturation is not applied in the visualisations throughout this work.

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